{"id":"W3149981730","doi":"10.1111/tran.12441","title":"Thinking algorithmically: The making of hegemonic knowledge in climate governance","year":2021,"lang":"en","type":"article","venue":"Transactions of the Institute of British Geographers","topic":"Ethics and Social Impacts of AI","field":"Social Sciences","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Hegemony; Corporate governance; Climate governance; Politics; Epistemology; Sociology; Environmental governance; Scholarship; Transformative learning; Computer science; Political science; Economics; Law; Management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001066104,0.00007678245,0.0002379778,0.00003475905,0.0004702039,0.00005216922,0.0006470149,0.0001323038,0.00003939282],"category_scores_gemma":[0.0001458308,0.00007612034,0.0003188316,0.001067834,0.001562128,0.0003726333,0.00003398064,0.0003510845,5.641521e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000455131,"about_ca_system_score_gemma":0.000449586,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01856167,"about_ca_topic_score_gemma":0.171489,"domain_scores_codex":[0.998555,0.0001804425,0.0003900616,0.0001478416,0.0004686487,0.0002579928],"domain_scores_gemma":[0.9990468,0.0001158496,0.0002344383,0.0002484644,0.0003287385,0.00002572034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001542061,0.003450824,0.03139398,0.001467879,0.001391771,0.0001003932,0.1576124,0.02240257,0.003735493,0.2437283,0.001721232,0.5328409],"study_design_scores_gemma":[0.006733991,0.0003643436,0.5641595,0.01901061,0.001270126,0.0001048307,0.1507923,0.001076452,0.006616136,0.1385431,0.1089824,0.002346242],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8893729,0.008500978,0.002122155,0.01938798,0.003213533,0.0006969886,0.0003688686,0.00006152555,0.07627501],"genre_scores_gemma":[0.9950123,0.003492386,0.001205117,0.0001198103,0.00003060106,0.000004569586,7.813526e-7,0.000008253991,0.0001261204],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5327656,"threshold_uncertainty_score":0.9879738,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01908322247225076,"score_gpt":0.3066779720690376,"score_spread":0.2875947495967868,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}